EFFECT OF RAINGAGE DENSITY ON RUNOFF SIMULATION MODELING by ABSTRACT

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1 EFFECT OF RAINGAGE DENSITY ON RUNOFF SIMULATION MODELING by Ben Urbonas, P.E., D.WRE 1, Michael Paul Jansekok 2, James C.Y. Guo, PhD, P.E. 3 ABSTRACT Rainfall and runoff data for a 3.08 square mile urban watershed in Denver, Colorado was used to investigate the effects of raingage density and hyetograph compositing on urban stormwater runoff simulation. This watershed has rainfall data from five raingages and flow data from gages, all in 5-minute time increments. This data were used to calibrate Urban Drainage and Flood Control District s version of EPA SWMM (v.2) model, namely, UDSWM. The calibrated model served two purposes; first, to examine the effects on runoff calculations using a single composite hyetograph for each of the 17 storms modeled and second, to determine the effects of raingage density on volume of runoff and peak flow simulations. The effects of raingage density were also investigated by processing several different combinations of raingage densities with the calibrated UDSWM model. This paper describes the findings of this study and discusses their implications for urban stormwater runoff modeling. INTRODUCTION The question that keeps arising is whether hydrologic computer modeling appropriately accounts for the temporal and spatial variations in rainfall patterns that occur in nature. If only one raingage record is used to represent the rainfall in a watershed, any given storm could easily concentrate its main intensities near the raingage and totally miss most of the watershed altogether. The resulting runoff-to-rainfall ratio could vary from either very high to very low, and attempting to establish any relationship between the two would be problematic, as much of past attempts have shown, especially for larger catchments. Some of the questions that arise and need better answers are: Does this rainfall/runoff ratio become more constant when raingage density increases? And what about those hydrologic models that require compositing of multi-gage rainfall hyetographs into a single rainfall hyetograph input record, such as the CUHP and TR-55 models? How does compositing or several rainfall records affect the calculated surface runoff? Is there a compositing technique that yields more realistic results? Much of what is written about the effects of raingage density and hyetograph compositing methods in runoff modeling revolves around synthetically manufactured hyetographs, namely, design storms. The authors were fortunate to have access to eight years of finely incremented (i.e., 5-minute time step) simultaneous rainfall and runoff records for a relatively small and stable urban watershed with a high raingage density. This paper attempts to show both the variance in runoff calculations that can occur when raingage density is increased or decreased and the effects of hyetograph compositing on stormwater runoff modeling of peak flows and runoff volumes. PREVIOUS INVESTIGATIONS Very little research has been done in compositing simultaneous rainfall records into single model input hyetographs and their effect on calculated stormwater discharges. The same is true for raingage density. An investigation of hyetograph compositions by Avon, Collins and Kibler (1974) was performed for a smaller watershed using two hypothetical, four time increment hyetographs. They recommended 1 Manager, Master Planning Program, Urban Drainage & Flood Control District, Denver, Colorado. 2 Sr. Programmer/Analysts, Oppenheimer Funds, Inc., Centennial, Colorado in Professor, University of Colorado at Denver, Denver, Colorado.

2 adopting the hyetograph pattern from one gage and compositing hyetographs using a peak pattern preservation technique. The results from the authors study reported in this paper using much more detailed simultaneous rainfall-runoff records appear not to support their conclusion. RAINFALL AND RUNOFF GAGES USED BY THIS STUDY Rainfall/runoff data recorded between 1979 and 1987 for the Harvard Gulch drainage watershed ware obtained from the U.S. Geologic Survey at the Denver Federal Center in Lakewood, CO. The data were collected under a cooperative agreement between the Urban Drainage and Flood Control District and USGS. Data from 2-flow gages and 5-rain gages were used in the investigations. Detailed records of rainfall were obtained using a recording tipping bucket raingage at each site and a flow-stage data collected at the two stations by digital recorders, which punch the data on 16-channel paper tape at 5-minute intervals. The locations of all gages are shown in Figure 1a, 1b and 1c that also depict the study watershed, its land uses, its major drainageway network, UDSWM catchments and UDSWM conveyance elements used to model this watershed. Flow Gage 1. HARVARD GULCH AT COLORADO BOULEVARD: USGS Gage Number Location: Latitude 39o40'08", longitude 104o56'32", in SE1/4 SE1/4 section 30, Township 4 S., Range 67 W. Denver City and County, on left bank 100ft (30m) upstream from S. Jackson St. and 400 ft (122m) north of E. Yale Ave. Digital flood-hydrograph recorder. This catchment drains residential area 1.12 mi HARVARD GULCH AT HARVARD PARK: USGS Gage Number Location: Latitude 39o40'21", longitude 104o58'35", in NW1/4 SW1/4 section 26 Township 4 S., Range 68W. Denver City and County, on left bank of Harvard Gulch 200 ft (61m) north of E. Harvard Ave. and 300 ft (91m) west of S. Ogden St., directly north of Porter Hospital. Gage records flood-hydrograph and rainfall records (dual digitals). Tributary watershed is a mix of residential and commercial land uses having an area of 3.08 mi 2. Rain Gage 1. HARVARD GULCH AT BRADLEY SCHOOL: USGS Gage Number Location: Latitude 39o39'48", longitude 104o55'50", in SE1/4 NE1/4 section 31 Township 4 S., Range 67W. Denver City and County, on east side of S. Dahlia St. against the Bradley School ground fence, 2640 ft (804 m) south of E. Yale Ave. 2. HARVARD GULCH AT BETHESDA HOSPITAL: USGS Gage Number Location: Latitude 39o40'28", longitude 104o56'02", in SE1/4 NW1/4 section 30 Township 4 S., Range 67W. Denver City and County, on the north edge of a parking lot 300 ft (91 m) east of hospital entrance at East Iliff and Cherry Street. 3. HARVARD GULCH AT UNIVERSITY PARK SCHOOL: USGS Gage Number Location: Latitude 37o40'28", longitude 104o56'55", in NW1/4 SE1/4 section 25 Township 4 S., Range 67W. Denver City and County on the school grounds next to the parking lot, 30 ft south of East Iliff and 1 block east of S. St Paul St. Established HARVARD GULCH AT SLAVEN SCHOOL: USGS Gage Number Location: Latitude 39o39'38", longitude 104o57'21", in SW1/4 NW1/4 section 36 Township 4 S., Range 68W. Denver City and County on north side of East Dartmouth, 30 ft. west of S. Clayton St.

3 5. HARVARD GULCH AT HARVARD PARK: (See Flow Gage Above - Item #2) Figure 1a. Harvard Gulch Catchment Land Use and Raingage Location Map.

4 Figure 1b. Harvard Gulch UDSWM Sub-Catchments.

5 Figure 1c. Harvard Gulch UDSWM Conveyance Elements.

6 VERIFICATION AND ADJUSTMENT OF USGS RATING CURVES Initial Runoff Volume Analyses Initial verification of runoff volumes for lower (at Harvard Park) and upper (at Colorado Blvd) watershed flow gages was performed. All storms within the eight-year record that had a minimum peak flow of 50 CFS and a minimum rainfall depth of 0.08 inches in any 5-minute period at either gage were used in the initial analysis. Forty-eight events between August '79 and September '87 met these criteria. Summaries of the data from these events are listed in Table 1. The Thiessen Polygon method was used to identify what fraction of the total watershed each raingage was assumed to cover. These resulting weighting factors varied between some of the events, as not all gages were operating during all of the forty-eight events. The total weighted rainfall depth for the entire watershed was calculated for each rainstorm incremental rainfall depths during the event by multiplying the depth at each gage by its appropriate weighting factor and them summing them for that increment it time. Table 2 shows the area weighting factors used for each raingage when different combinations of gages were in operation. Calculating the volume under each storm runoff hydrograph and dividing it by the tributary area produced the total runoff inches for each of the storm events. Hydrographs for which data points were truncated on the receding limb were extrapolated using semi log - log paper (Time vs. log of Flow). Runoff-volume to Rainfall-volume ratio pairs were plotted against Rainfall inches for both total and upper watersheds. Data regression was performed to arrive at a best-fit line to graphically represent each watershed's runoff coefficient. Harvard Gulch at Harvard Park and Harvard Gulch at Colorado watersheds were found to have runoff coefficients of 0.15 and 0.34 respectively (see Figures 2 and 3). The upper watershed had a coefficient that appeared to be within a reasonable range for the land uses within it. However, the total watershed tributary to the gage at Harvard Park had a coefficient that was clearly too low for its land uses. Because of this finding it was decided to test the validity of each flow gage-rating curve at this location. New channel Cross Sections Fourteen channel cross-sections were surveyed in a 250 foot stretch both upstream and downstream of the Colorado Boulevard flow gage site. Sixteen channel cross-sections were taken in an 850-foot reach at the Harvard Park flow gage site. Retardance Curve Interpolations for Incremental Stage Values of Manning's n. Each cross section was divided into 4 or 5 horizontal sections by major changes in topography or grass type. Within each horizontal section 3 vertical divisions (i.e. Left channel, Right channel, and Center channel) were created beginning with the horizontal section directly above the trickle channel. A slope of 1:1 reduced the effects of a vertical boundary of the central channel as water depth increased. Incremental values for Manning's n were then developed using the U.S. Soil Conservation Service Vegetal Retardance n vs. VR curve B for high vegetal retardance. HEC-2 Backwater Curve Calculations The U.S. Army Corp of Engineers' HEC-2 program was used to calculate backwater curve profiles via the Standard Step Method for both flow gage sites. To verify the elevation datum of the cross-sections relative to the flow gage readings, a bucket of water was placed under each flow gage until a reading was observed on the flow gage stage indicator. This reading was recorded along with the corresponding water surface elevation on the flow gage tube. By processing several different flows ranging from 50 to 1000 CFS with the HEC-2 program and recording the corresponding backwater elevation on the flow gage tube, a set of stage-discharge rating curves were developed and compared with the existing USGS rating curves for both gages.

7 RAINFALL (INCHES) Figure 2. Initial Runoff Coefficient Analysis Harvard Gulch at Harvard Park RAINFALL (INCHES) Figure 3. Initial Runoff Coefficient Analysis Harvard Gulch at Colorado Blvd.

8 TABLE 1 INITIAL SUMMARY ANALYSIS OF RAINFALL/RUNOFF AT HARVARD GULCH Cumulative Rainfall in Inches Weighted Rain(in.) DATE OF EVENT H. PARK (1) SLAVEN (2) BETHESDA (3) BRADLEY (4) U.PK.SCH COLO. H.PARK (ALL 5) 16-Aug NO DATA NO GAGE May NO GAGE Jul NO GAGE NO GAGE Aug NO GAGE NO GAGE Aug NO GAGE NO GAGE May NO DATA May Jul Jul May NO DATA Jun NO DATA Jun NO DATA Jul NO DATA Jul NO DATA Jun Jun Jun GAGE Jun Jun Jul GAGE Aug Aug Aug Sep May Jun Jun Jul Jul Jul Ju Aug Sep Sep Apr-86 GAGE Jun-86 GAGE GAGE Jun GAGE Jun-86 GAGE GAGE Jul Jun Jun Sep Aug GAGE Aug GAGE Aug GAGE Jun May May

9 TABLE 1 (continued) Runoff Volume Revised Runoff Rev. r.o. Recorded Peak Revised DATE OF (In.) Volume(In)/Rain(In) Vol. (In) Flow (CFS) Flow EVENT H H COLO. H PARK COLO. H PARK H PARK COLO. PARK PARK H PARK 16-Aug May Jul Aug Aug May-81 No Data No Data No Data May Jul Jul May Jun Jun Jul-82 No Data No Data No Data Jul-82 No Data No Data No Data Jun Jun Jun Jun Jun Jul Aug Aug Aug Sep May Jun Jun Jul Jul Jul Jul Aug Sep Sep Apr Jun Jun Jun Jul Jun Jun Sep Aug Aug Aug Jun May May

10 TABLE 2 RAIN GAGE WEIGHTING FACTORS VIA THIESSEN POLYGON METHOD GAGES WEIGHING FACTOR IN PERCENT X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 2 X X X X 2 X Gage Locations: 1. Harvard Park 2. Slaven 3. Bethesda 4. Bradley 5. University Park Adjustment of USGS Rating Curves Based on the results obtained from HEC-2 output and field recorded flow values, adjustments were made to the Harvard Park rating curve. USGS flow values were found to be too low for corresponding gage heights. Three regression equations were developed using the filed data and HEC-2 runs to bring all recorded USGS flow data in line with the revised rating curve. The regression equations were as follows: When USGS flows X Adjusted flow Y is given by <= 20 Y = 2 X 20 < X < 150 Y = X X >= 150 Y = X X In which, X = USGS flow (cfs) and Y = Adjusted flow (CFS) The revised runoff/rainfall ratios vs. rainfall results indicate that average runoff coefficient for the Harvard Gulch at Harvard Park watershed is approximately This value was judged to be more representative than the 0.15 obtained using uncorrected flow data. No adjustments were made to the Colorado Boulevard flow gage-rating curve. SELECTION AND PREPARATION OF ANALYSIS DATA Minimum Rainfall & Runoff Criteria for this Study Seventeen storms were selected for further hydrologic analysis based on the following criteria: 1) Five (5) raingages and two (2) flow gages must be reporting during the storm. 2) Minimum recorded rainfall at any gage must equal or exceed 0.08 inches during at least one 5 minute period within a storm. 3) The recorded peak flow at any gage must equal or exceed 50 CFS. Preparation of UDSWM Model Stormwater drainage system maps from Denver Wastewater Control Division provided the initial basis for both sub-catchment definitions and the definitions of drainage system networks. Major catchment boundaries and the degree of imperviousness were field verified by UDFCD personnel in 1979 and 1980.

11 As illustrated in Figures 1b, the Harvard Gulch watershed was sub-divided into 59 sub-catchments. Of this number, 23 were for the upper watershed tributary to the gage at Colorado Boulevard. Average values for imperviousness, slope, tributary width, Manning's n, etc. were calculated for each subcatchment area and were input into the UDSWM model. The drainage conveyance element layout, which consisted of 78 conveyance elements, 35 providing drainage for the upper watershed is illustrated in Figure 1c. Conveyance elements were divided into five types as either pipe, pipe with overflow, channel, channel with overflow, or non-routing. One detention element was incorporated into the model to reflect field verified conditions, namely the sump area at Yale Avenue and Colorado Boulevard. The Thiessen Polygon Method was used to assign each sub-catchment to a specific raingage. Raingage assignments to sub-catchments for two separate simulation scenarios are listed in the UDSWM model input. Calibration of UDSWM Model Two separate models were created for the Harvard Gulch Watershed, namely an upper and a total watershed model. The upper watershed model consisted of the watershed area east of Colorado Blvd. Each one of the 17 selected storms was processed through the UDSWM model for both watersheds. Calculated runoff volumes and peak discharges were plotted against observed values. Data regression was used to draw a best-fit line through plotted points. Adjustments were made to pervious, imperviousness, Manning's n, sub-catchment tributary width, etc. values and the model was rerun for each of the 17 storms, until each best-fit line approximated 45 degrees between modeled results and recoded data for both peak flows and runoff volumes in each watershed. These UDSWM Model calibration results are shown in Figures 4a, 4b, 5a and 5b for the Colorado Boulevard and Harvard Park gaging sites respectively. USE OF CALIBRATED SWMM MODEL Investigating the Effects of Raingage Density For the total watershed, five increasing raingage density combinations were used to show the effects of raingage density on peak flow and runoff volumes. Two different scenarios of these five-gage combinations were simulated. For the upper watershed, three combinations of rain gage conditions were simulated. All these simulation scenarios are shown Tables 3. Again the Thiessen Polygon Method was applied to assign each sub-catchment to a specific raingage for each of the above-specified conditions. The complete list of rain gage assignments to sub-catchments can be found in the model input file. Normalized values of deviations from the calibrated 5 rain gage (2 gage at Colorado Blvd) SWMM values for peak flow and runoff volumes were plotted against number of raingages used in simulations for both upper and total watersheds. Comparisons were made for (1) 5-gage calibrated SWMM Model vs. 1 through 4 gage for both scenarios and (2) actual recorded data vs. 1 through 5 gage calibrated SWMM Model, also for both scenarios. The normalizing method for defining percent variance is described by the following equation: Vi = [(Ri-Rci)/Rci]*100 in which, Vi = variance from the calibrated five rain gage peak flow or volume for storm i, Ri - runoff peak or volume for the test run for storm i, Rci = runoff peak or volume for calibrated model or recorded data for storm i.

12 400.0 UDSWM Calculated Peak Flow - CFS Perfect Fit Line R 2 = Best Regression Fit R 2 = Recorded Peak Flow - CFS Figure 4a. Calibration of UDSWM to Peak Flows at Colorado Boulevard Gage UDSWM Calculated Volume - Inches Best Regression Fit R 2 = Observed Volume - Inches Figure 4b. Calibration of UDSWM to Runoff Volume at Colorado Boulevard Gage.

13 UDSWM Calculated Peak Flow - CFS Best Regression Fit R 2 = Observed Peak Flows - CFS Figure 5a. Calibration of UDSWM to Peak Flows at Harvard Park Gage. UDSWM Calculated Runoff Volume - Inches Perfect Fit Line R 2 = Observed Runoff Volume - Inches Figure 5b. Calibration of UDSWM to Runoff Volume at Harvard Park Gage.

14 TABLE 3. GAGES SIMULATING RUNOFF UNDER DIFFERENT GAGE AVAILABILITY SCENARIOS. GAGES USED TO MODEL SCENARIO #1 FOR THE TOTAL WATERSHED TRIBUTARY TO HARVARD PARK Run Harvard Bradley University Slaven Bethesda Number Park Park 1 X 2 X X 3 X X X 4 X X X X 5 X X X X X GAGES USED TO MODEL SCENARIO #2 FOR THE TOTAL WATERSHED TRIBUTARY TO HARVARD PARK 1 X 2 X X 3 X X X 4 X X X X 5 X X X X X GAGES USED TO MODEL THE UPPER WATERSHED TRIBUTARY TO COLORADO BLVD. 1 X 2 X 3 X X Composite Type Comparisons Peak preservation and across composite type comparisons of recorded hyetographs at multiple gages were performed for the upper watershed and for the total watershed. The Thiessen Polygon Method was used to assign a weighting factor to each raingage hyetograph record and transform incremental rainfall from each raingage into one combined composite storm. Each storm was processed through the upper and total watershed calibrated UDSWM Model. Hydrograph Comparisons For both upper and lower watersheds the following hydrograph comparisons were made using eight storms: 1. UDSWM simulated flows at Harvard Park calibrated using all 5 raingages (and at Colorado Blvd. Calibrated using 2 raingages) vs. field observed hydrographs. 2. UDSWM simulated flows at Harvard Park calibrated using all 5 raingages (or flow at Colorado Blvd. Calibrated using 2 raingages) vs. simulated flows using a single composite rainfall hyetographs for each storm using the following two methods of compositing: a. Area weighted composite at each time increment, no consideration for highest peak rainfall b. Area weighted composite at each time increment after aligning each hyetograph peak rainfall increments to be at the same time increment (i.e., peak preservation). PRESENTATION OF DATA AND DISCUSSION OF RESULTS Effect of Raingage Density and Location Tables 4 and 5 compare the simulated peak flows and Tables 6 and 7 compare the simulated runoff volume for two scenarios of distributions of one through four raingage combinations used in these simulation against the simulated peak flows for the entire watershed that were obtained using the five raingage calibrated UDSWM model. Similar comparisons are shown in Tables 8 and 9 for the upper watershed, except here the comparisons are against the two gage calibrated UDSWM model. All these

15 tables show the percent variation in the mean, percent range in the variations and the standard deviation in the variation percentages from the five-gage simulations. TABLE 4. HARVARD GULCH AT HARVARD PARK - PEAK FLOW (SCENARIO #1) PERCENT FROM 5 GAGE CALIBRATED RUN PEAK FLOWS NO. OF GAGES to to to to TABLE 5. HARVARD GULCH AT H. PARK - PEAK FLOW (SCENARIO #2) PERCENT FROM 5 GAGE CALIBRATED RUN PEAK FLOWS NO. OF GAGES to to to to to TABLE 6. HARVARD GULCH AT H. PARK - RUNOFF VOLUME (SCENARIO #1) PERCENT FROM 5 GAGE CALIBRATED RUN VOLUMES NO. OF GAGES to to to to to TABLE 7. HARVARD GULCH AT H. PARK - RUNOFF VOLUME (SCENARIO #2) PERCENT FROM 5 GAGE CALIBRATED RUN VOLUMES NO. OF GAGES to to to to to TABLE 8. HARVARD GULCH AT COLORADO BLVD. - PEAK FLOW PERCENT FROM 2 GAGE CALIBRATED RUN PEAK FLOWS GAGE Bethesda to Bradley to TABLE 9. HARVARD GULCH AT COLORADO BLVD. - RUNOFF VOLUME PERCENT FROM 2 GAGE CALIBRATED RUN VOLUMES GAGE Bethesda to Bradley to

16 Similar comparisons for the total Harvard Gulch watershed are made for the simulated peak flows and runoff volumes results for the two sets of raingage distribution scenarios. For each scenario one through five raingages were used and the simulated results were compared against the observed values at the upper gaging site and the gaging site for the total watershed. Tables 10 and 13 show these comparisons, while Tables 14 and 15 provide similar comparisons for the upper watershed. TABLE 10. HARVARD GULCH AT H. PARK - PEAK FLOW (SCENARIO #1) PERCENT FROM ACTUAL RECORDED PEAKS NO. OF GAGES to Harvard Park only to Harvard Park & Bradley to to to TABLE 11. HARVARD GULCH AT H. PARK - PEAK FLOW (SCENARIO #2) PERCENT FROM ACTUAL RECORDED PEAKS NO. OF GAGES to Slaven only to Slaven & Bethesda to to to TABLE HARVARD GULCH AT H. PARK - RUNOFF VOLUMES (SCENARIO #1) PERCENT FROM ACTUAL RECORDED VOLUMES NO. OF GAGES RANGE MEAN STANDARD to to to to to TABLE HARVARD GULCH AT H. PARK - RUNOFF VOLUMES (SCENARIO #2) PERCENT FROM ACTUAL RECORDED VOLUMES NO. OF GAGES RANGE MEAN STANDARD to to to to to TABLE HARVARD GULCH AT COLORADO BLVD. - PEAK FLOW PERCENT FROM ACTUAL RECORDED PEAKS GAGE Bethesda to Bradley to Gage to

17 TABLE HARVARD GULCH AT COLORADO BLVD. - RUNOFF VOLUMES PERCENT FROM ACTUAL RECORDED VOLUMES GAGE Bethesda to Bradley to Gage to Effect of Composite Type One of the notable trends found in this study is the tendency for the composite hyetographs to somewhat underestimate peak flows and runoff volumes. This is shown in Tables 16 and 19. The variation of peak flows from the calibrated five gage model were observed as low as -65 percent (-28 percent for upper watershed model) and for runoff volumes were observed as low as -20 percent (-10 percent for upper watershed model). Standard deviations and the mean for composite type comparisons are also shown in these tables. There was very little difference observed in the results between the two compositing methods investigated. This, however, may be because of the population of rainstorms used in the studies. (For example, peak intensities for most of the 17 selected storm events rarely varied in time by more than 15 minutes from the mean). TABLE 16. HARVARD GULCH AT H. PARK - PEAK FLOW (COMPOSITE TYPES) PERCENT FROM 5 GAGE CALIBRATED RUN COMPOSITE TYPE Peak Preservation to Across to TABLE 17. HARVARD GULCH AT H. PARK - RUNOFF IN. (COMPOSITE TYPES) PERCENT FROM 5 GAGE CALIBRATED RUN COMPOSITE TYPE Peak Preservation to Across to TABLE 18. HARVARD GULCH AT CO. BLVD. - PEAK FLOW (COMPOSITE TYPES) PERCENT FROM 2 GAGE CALIBRATED RUN COMPOSITE TYPE Peak Preservation to Across to TABLE 19. HARVARD GULCH AT CO. BLVD. - RUNOFF IN. (COMPOSITE TYPES) PERCENT FROM 2 GAGE CALIBRATED RUN COMPOSITE TYPE Peak Preservation to Across to CONCLUSIONS Regarding Raingage Density and Location It is clear that variation of the simulated peak flows and volumes increased as the rain gage density decreased. The largest variations occurred when only one rain gage was used to represent the rainfall

18 over the entire watershed. The calculated peak flow and runoff volumes varied by as much as 91 to +146 percent from the calibrated five gage model runs (over -100 and +100 percent for the calibrated two gage model for the upper watershed). The calculated volumes varied by as much as 99 to percent from the five gage calibrated model results, depending on the scenario of gages used for simulation. The calculated peak flows varied by as much as 100 to percent when compared to the observed peaks (-93 to +100 percent for the upper watershed). The calculated volumes varied by as much as 99 to percent from the observed values, depending on the scenario of gages used for simulation. The largest shift in the mean for the simulated peaks flow using the 17 storms was found to be within 25 percent of the average obtained from the 5-gage calibrated model runs. The maximum variation in the simulated mean peak flows was within 27 percent from the field-observed data and within 23 percent when compared to the observed volumes. The location of the raingages had a noticeable impact on how the simulated results varied from the fieldobserved data. This variation was largest when only one gage was used. The least amount of error occurred when the gage was located closest to the centroid of the watershed and the largest when it was located at the downstream end where the flow gage was located. When two raingages were used, having the raingages positions near the two ends of the watershed resulted in least variances (Scenario 2). This finding implies that if two gages are used within the watershed of similar size they are best located within the upper and lower 1/3 portions of the watershed. When comparing the simulated results to the observed data it was observed that when the gage density exceeded approximately one (1) gage per square mile, very little change occurred in the range of variations in the results or in the standard deviation for the five-gage simulated case. Similar results were also seen when simulated results were compared to the observed values, where the variability of the simulated results did not change significantly after a density of one raingage per square mile was reached. From these observations one can conclude that a raingage density of one (1) raingage per square mile does not have to be exceeded to improve on the simulation results. Apparently factors other that raingage densities were in play that affect the accuracy of peak flow or runoff volume simulation for individual storm events. When lesser raingage densities than one per square mile are used, the placement and distribution of the raingages can have a significant effect on the accuracy of simulated results. The authors postulate that the rainstorm footprints and the direction of the storm track across the watershed (i.e., watershed orientation relative to the track of the storm) affect the accuracy of simulated results for any given rainfallrunoff event. In conclusion, raingage density plays a very important role in the accuracy of hydrologic modeling. At the same time, it appears that if a sufficient number of rainfall events are used, the averages of peak flows and runoff volumes can be reasonable close to the averages obtained using either multi-gage simulation results or the observed data. Although it appears that one raingage per square mile is sufficient density needed to achieve most representative simulations of rainfall-runoff events, this number will probably vary with climatologic region and the types of storms that dominate it. At the same time, judicious placement of fewer raingages (i.e., 1.5 gages per square mile) can also achieve reasonable simulations of individual events. Although these findings are appropriate for the Denver, Colorado region, one that is dominated mostly by convective and frontal storms, it is probably not the case for other regions such as Seattle, Washington where rainfall patterns are dominated by lower intensity area-wide upslope storms. As to the number of rainfall events needed to insure that the averages of simulated results are realistic has yet to be determined, but appear the numbers used in this study, namely 17 selected storm events, were sufficient

19 to achieve this goal. This number is also probably dependent on the types of storms experienced at the study site. Regarding Hyetograph Compositing Using Several Gages Very little difference was found in peak flow and runoff volume simulations between the two rainfall compositing techniques tested, namely compositing rainfall straight across or compositing using peak preservation. Both methods tended to underestimate the simulated peak flows and volumes when compared to field-recorded data. Some hydrologic models require incremental rainfall depths to be composite into a single input hyetograph when more than one raingage record is available. These numerical models are then calibrated against observed data by modifying runoff coefficients and other parameters in order to increase the calculated volumes and peaks to bring calculated values in line with observed data. One possible problem in calibrating a model using composite rainfall data is that if other recorded point rainfall or long-term non-composite rainfall data are used later with the model, the calculated volumes and peak flows are likely to be overestimated. Because the model was calibrated using composite hyetographs which appear to underestimate peak flows and volumes, the percentage by which calibration parameters are adjusted to increase calculated peaks and volumes will be the percentage by which the use of non-composite rainfall data later will overestimate the peak flows and volumes. It is this possibility of overestimating during long term simulations that should be considered by modelers when calibrating models using composite hyetographs, particularly when studying larger urban watersheds.